Generative modeling of complex signals is one of the fundamental challenges of computational cognition. Powerful generative models could provide the foundation of the imaginative capabilities of future machine intelligence. One approach to generative modeling is to posit the existence of a density \(q(x)\)  of signals \(x\)  that generates data samples, and to learn \(q(x)\)  using a flexible model \(p(x;\theta )\) , where \(\theta \)  is a model parameter. This approach is taken by Energy-Based Models (EBMs) [1]}, normalizing flows [2]}, score-based models [3]}, [4]}, and auto-regressive models [5]}, as well as by Variational Auto-encoders (VAEs) [6]} using a joint model \(p(x,z;\theta )\) . In this work we consider modeling \(q(x)\)  using an EBM density \(p(x;\theta )\)  for image signals \(x\) . See Appendix REF  for a brief review of Maximum Likelihood learning with an EBM.
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